Octadecyl Amine Functionalized Graphene Oxide towards Hydrophobic Chemical Resistant Epoxy Nanocomposites
Bibliographic record
Abstract
Abstract In this research study, graphene oxide (GO) was synthesized using an improved Hummers method and thence oxygen‐based functional groups were replaced with octadecyl amine. Afterwards, epoxy‐based nanocomposites containing GO and GO‐octadecyl amine (GO‐ODA) were fabricated via a vacuum shock technique. The chemical resistance of the fabricated nanocomposites against various solvents, namely xylene, toluene and distilled water, was investigated and the effect of chemical degradation on the hardness and structural stability of the nanocomposites was examined. Besides, SEM, contact angle and AFM analyses were conducted to evaluate morphology, hydrophobicity and roughness of the nanocomposites prior to and following the chemical degradation. The obtained results illustrated that the reinforcement of the epoxy‐based nanocomposites with GO‐ODA and GO, in order, enhanced chemical resistance, hardness, roughness, and hydrophobicity of the nanocomposites. For instance, after incorporating GO and GO‐ODA into the epoxy resin, the average roughness of the neat epoxy resin reduced 84.0% and 94.1%, the hardness of the neat epoxy resin increased from 80 to 82 and 86 kgf/mm 2 , and the contact angle of the epoxy resin changed from 76° to 71° and 85°, respectively. In brief, outcome of this study reveals that GO‐ODA can be used as an efficient filler to develop effective hydrophobic polymeric shields against chemical degradation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".